A systematic framework of constructing surrogate model for slider track peeling strength prediction  

作　　者：DONG XingJian CHEN Qian LIU WenBo WANG Dong PENG ZhiKe MENG Guang 

机构地区：[1]State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai 200240,China [2]KEIPER Seating Mechanisms Co.,Ltd.,Soochow 215500,China [3]Department of Industrial Engineering and Management,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Science China(Technological Sciences)》2024年第10期3261-3274,共14页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12272219 and 12121002).

摘　　要：Peeling strength can comprehensively reflect slider track safety and is crucial in car seat safety assessments.Current methods for determining slider peeling strength are primarily physical testing and numerical simulation.However,these methods encounter the potential challenges of high costs and overlong time consumption which have not been adequately addressed.Therefore,the efficient and low-cost surrogate model emerges as a promising solution.Nevertheless,currently used surrogate models suffer from inefficiencies and complexity in data sampling,lack of robustness in local model predictions,and isolation between data sampling and model prediction.To overcome these challenges,this paper aims to set up a systematic framework for slider track peeling strength prediction,including sensitivity analysis,dataset sampling,and model prediction.Specifically,the interpretable linear regression is performed to identify the sensitivity of various geometric variables to peeling strength.Based on the variable sensitivity,a distance metric is constructed to measure the disparity of different variable groups.Then,the sparsity-targeted sampling(STS)is proposed to formulate a representative dataset.Finally,the sequentially selected local weighted linear regression(SLWLR)is designed to achieve accurate track peeling strength prediction.Additionally,a quantitative cost assessment of the supplementary dataset is proposed by utilizing the minimum adjacent sample distance as a mediator.Experimental results validate the efficacy of sequential selection and the weighting mechanism in enhancing localization robustness.Furthermore,the proposed SLWLR method surpasses similar approaches and other common surrogate methods in terms of prediction performance and data quantity requirements,achieving an average absolute error of 3.3 kN in the simulated test dataset.

关 键 词：slider track peeling strength surrogate model sensitivity analysis data sampling local weighted linear regression 

分 类 号：U463.836[机械工程—车辆工程]